Method and system for monioring sport related fitness by estimating muscle power and joint force of limbs

ABSTRACT

The present invention relates to a method and system for monitoring sport related fitness by estimating muscle power and joint force of limbs, in which the system comprises a sensing module and a force/track detection module, wherein sensor values from the sensing module are fed to the force/track detection module to be used as base for estimating feature parameters and classifying a motion series relating to muscle power and joint force of limbs so as to obtain skill-related fitness parameters corresponding to the sensing of the sensor module.

CROSS-REFERENCE TO RELATED APPLICATIONS

This non-provisional application is a divisional application of U.S.patent application Ser. No. 12/696,396, filed Jan. 29, 2010, whichitself claims priority under 35 U.S.C. §119(a) on Patent Application No.098133931 filed in Taiwan, R.O.C. on Oct. 7, 2009, the entire contentsof which are hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to a method and system for monitoringsport related fitness, and more particularly, to a fitness monitoringmethod and system capable of performing a fitness evaluation at any timeand place since it is not only configured with a monitor platformadapted to be arranged in any common household living space, but alsowith a portable fitness detection module.

TECHNICAL BACKGROUND

Research has shown that a large number of the health problems in societyare either caused in whole or in part by an unhealthy lifestyle thatoften result in poor eating habits, high stress levels, lack ofexercise, poor sleep habits, and so on. Recognizing this fact, the fieldof physical fitness assessment and testing has seen an increasing demandwith rising public interest in physical fitness and the relevance ofperformance to soldiers, firefighters, athletes, and the like. Moreover,it is used in the field of patient rehabilitation. These days, physicalfitness is considered a measure of the body's ability to functionefficiently and effectively in work and leisure activities, to behealthy, to resist hypokinetic diseases, and to meet emergencysituations. Accordingly, a general-purpose physical fitness program mustaddress the following essentials: cardio-respiratory endurance, muscularstrength and muscular endurance, joint flexibility, and bodycomposition, which are health related issues, However, the sport-relatedissues are also included, which are muscular power, agility, speed andcoordination, etc. Scoring high on those physical fitness assessmentsusually indicates better heath and better exercise performance. However,good physical fitness is not easy to obtain and certainly can not beachieved overnight. It requires a person to maintain a healthy lifestylewhile exercise in a regular basis. Nevertheless, for motivating a personto live a healthier life and exercise regularly, it would be a greathelp if data of detailed physical fitness can be provided to that personin a daily basis to be used as a guide for achieving a healthierlifestyle, for monitoring progress, and for brainstorming solutions whenproblems arise.

Conventionally, a fitness assessment is a series of measurements thathelp determine physical fitness. The basic formula of any conventionalfitness assessment is to evaluate body mass index (BMI), resting heartrate and blood pressure, and aerobic fitness before, during or after amoderate workout that may last a specific period of time. There are manyfitness assessment products available on the market, including weightscales with BMI monitoring ability and tread mills with heartrate/respiratory monitoring ability, and so on. However, all thosefitness assessment products have the following shortcomings:

(1) The fitness assessment can only be conducted on the specificexercise platform;

(2) The result of the fitness assessment can be very subjective since itis provided from assessors sometimes only basing on the readings fromthose fitness assessment products while those assessors might not beparticularly well trained;

(3) Those products with balance evaluation system are imported that arebulky and expensive;

(4) It is required for the user to be applied by at least a set ofelectrodes for enabling a wireless or wired EMG test to be perform; Inthose conventional fitness assessment products, it is common to use agame console, such as Wii fit, for motivating its users to perform thedesignated exercises. However, such game console neither is notappropriate for elders, nor is not therapeutic effective in clinicalpractice;

(5) There is no dynamic balance monitoring for the control of thenervous, muscular and skeletal systems relating to the motions of upperand lower limbs that is available in conventional fitness assessmentproducts with game console; and

(6) The conventional fitness assessment products with game console arenot designed with the ability relating to the recognizing of motionaccuracy and coordination.

TECHNICAL SUMMARY

The present disclosure relates to a fitness monitoring method and systemcapable of performing a fitness evaluation at any time and place sinceit is not only configured with a monitor platform adapted to be arrangedin any common household living space, but also with a portable fitnessdetection module.

In an exemplary embodiment, the present disclosure provides a method andsystem for monitoring sport related fitness, in which the systemcomprises: a sensing module and a force/track detection module, whereinsensor values from the sensing module are fed to the force/trackdetection module to be used as base for estimating feature parametersand classifying a motion series relating to muscle power and joint forceof limbs so as to obtain skill-related fitness parameters correspondingto the sensing of the sensor module.

Further scope of applicability of the present application will becomemore apparent from the detailed description given hereinafter. However,it should be understood that the detailed description and specificexamples, while indicating exemplary embodiments of the disclosure, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the disclosure will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given herein below and the accompanying drawingswhich are given by way of illustration only, and thus are not limitativeof the present disclosure and wherein:

FIG. 1 is a block diagram of a system for monitoring sport relatedfitness according to an exemplary embodiment of the present disclosure.

FIG. 2 is a schematic diagram showing the coordination of shanks andthighs of lower limbs in an ergonomic model used in the presentdisclosure.

FIG. 3 is a schematic diagram showing the relationship betweensport-related fitness and a stiffness elliptic inclusion according tothe present disclosure.

FIG. 4 is a schematic diagram showing an identification projection ofthe present disclosure.

FIG. 5 shows a motion of a lower limb with respect to the variation offorce direction during the motion according to the present disclosure.

FIG. 6 is a schematic diagram showing the detection of a lower limbmotion according to an embodiment of the present disclosure.

FIG. 7 is a schematic diagram showing how the joint angles in lower limbare related to each other.

FIG. 8 is a schematic diagram showing the detection of an upper limbmotion according to an embodiment of the present disclosure.

FIG. 9 is a schematic diagram showing how the joint angles in upper limbare related to each other.

DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

For your esteemed members of reviewing committee to further understandand recognize the fulfilled functions and structural characteristics ofthe disclosure, several exemplary embodiments cooperating with detaileddescription are presented as the follows.

Please refer to FIG. 1, which is a block diagram of a system formonitoring sport related fitness according to an exemplary embodiment ofthe present disclosure. IN FIG. 1, the system for monitoring sportrelated fitness is primarily composed of: a sensing module 10 andforce/track detection module 20.

The sensing module 10 is configured with a pressure detecting unit 11, aposition detecting unit 12 and a processing unit 13, in which thepressure detecting unit 11, being used for sensing a value relating topressure, can be a device selected from the group consisting of: apressure mat, a foot pad, and a force plate, that is provided for a userto step thereon so as to generate a pressure value accordingly; theposition detecting unit 12, being used for sensing a value relating tomotion track, can be a device including at least one device selectedfrom the group consisting of: accelerometers and gyroscopes that can beadapted to be arranged at an arm, an elbow, waist, or a knee of a user;and the processing unit 13 is used for fetching and synchronizing thesensor values, such as the aforesaid pressure values from the pressuredetecting unit 11 and the motion track values from the positiondetecting unit 12.

It is noted that by arranging the pressure detecting unit 11 and theposition detecting unit 12 at different positions of a user, the limbmotions of the user capable of being detected thereby can be different.For any human body, the force/track detection module 10 is enabled totracking an exercise performed by the limbs of the user, which includesupper limbs such as arms and lower limbs such as legs, while thedetected track is resulting from at least one exercise selecting fromthe group consisting of: the stretching exercise of the upper limbs andthe stretching exercise of the lower limbs. Moreover, the stretchingexercise of the upper limbs includes at least one movement selected fromthe group consisting of: lifting a heavy object, pushing/pulling,punching, racket swinging, and pitching; and the stretching exercise ofthe lower limbs includes at least one movement selected from the groupconsisting of: one foot lifting, one leg squatting, one leg standing,two legs standing. By attaching the position detecting unit 12 to oneupper limb or lower limb of the user, the motion track of such upperlimb or lower limb can be sensed thereby. In the present disclosure, theuser is allowed to have one of his/her upper limb, one of his/her lowerlimb, both of his/her upper limb, both of his/her lower limb, or evenone selected upper limb and one selected lower limb, to be mounted bythe position detecting unit 12 at will. In addition, when the user,having the position detecting unit 12 attached therein, is stepped onthe pressure detecting unit 11 while performing an exercise, the forcevariation relating to the exercise of the user can be detected therebyas simultaneously the motion track relating to the exercise is detectedby the position detecting unit 12.

The force/track detection module 20 is provided for receive the sensorvalues from the sensing module 10 to be used as base for estimatingfeature parameters and classifying a motion series relating to musclepower and joint force of the limbs so as to obtain fitness parameterscorresponding to the sensing of the sensor module. Moreover, theforce/track detection module 20 includes a parameter calculation module21 and a motion identification module 22.

The parameter calculation module 21 is primarily composed of a featurecapturing unit 211 and an ergonomic model database 212. The featurecapturing unit 211 is used for fetching the sensor values from thesensing module to be used in an estimation for obtaining the featureparameters relating to muscle power and joint force of the limbs, inwhich each feature parameter is related to at least one value selectedfrom the group consisting of: gravity center of human body, biped centerof mass, displacement of the center of gravity, direction, speed,distance traveled, relative position, coefficient of rigidity, jointangle, variation of joint angle, muscle power. The ergonomic modeldatabase 212 is used for storing data relating to ergonomic models to beused as basis for estimating the feature parameters.

The motion identification module 22 is composed of a motion modeldatabase 221, a match unit 222, a fitness parameter database 223, acomparison unit 224 and a notification unit 225. The motion modeldatabase 221 has the at least one motion track model stored therein,whereas each motion track model is at least one model selected from thegroup consisting of: a motion track template, a motion track statisticmodel, a motion track probabilistic model. The match unit 222 is usedfor performing the matching calculation, such as a distance calculationor a similarity calculation, upon the feature parameter resulting fromthe estimation of the parameter calculation module 21 and the at leastone motion track models form the motion model database 221 so as toobtain an optimal motion track corresponding to the feature parametersto be classified into the motion series that is used for estimating thefitness parameters. Moreover, the fitness parameter is related to atleast one value selected from the group consisting of: muscularstrength, muscular endurance, agility, flexibility, muscle power, speed,and balance.

The fitness parameter database 223 is provided for storing skill-relatedfitness parameters while providing the stored skill-related fitnessparameters to be compared with the fitness parameters obtained from theestimation of the motion identification module 22. The comparison unit224 is used for comparing the fitness parameters obtained from matchunit 222 with the skill-related fitness parameters stored in the fitnessparameter database 223 so as to output a comparison result accordingly.Moreover, the notification unit 225 is used for displaying thecomparison result as it is electrically connected to the comparison unit224, and thus the notification unit 225 can be a device selected fromthe group consisting of: cellular phones, personal digital assistants(PDAs), computers, speakers, alarms, indication lamps, and otheraudio/video devices, that is capable of connecting to the comparisonunit 224 in a wired or wireless manner, i.e. by cable or through anetwork system for instance.

Please refer to FIG. 2, which is a schematic diagram showing thecoordination of shanks and thighs of lower limbs in an ergonomic modelused in the present disclosure. In FIG. 2, each thigh 31 is referred asa first limb L1 while the corresponding shank 32 along with the foot 33are referred as a second limb L2, in which the upper end of the firstlimb L1 is connected to a widesence stationary end 34 whereas thewidesence stationary end 34 includes the hip joint, the pelvic cavityand the lumbar vertebra; and the lower end of the first limb L1 isconnected to the knee joint 35. Moreover, the upper end of the secondlimb L2 is connected to the knee joint 35 while the lower end thereof isconnected to the foot 33 as the foot 33 is stepping on a planar surface36. When the foot 35 is subjected to a force of any direction, therewill be a reacting force acting on the knee joint 35 according to theNewton's third laws of motion, and thus, in order to keep balance, it srequired for the knee joint 35 to generate a force for counterbalancingthe reacting force. At this moment, since the station end 34 is in thestatus of widesence stationary, only the knee joint 35 will be caused torotate by the torque resulting from the force of counterbalancing thatthe addition of the displacement vectors relating to the knee joint 35and the foot 33 will construct a nonlinear relationship. Therefore, withrespect to the rigidity of the foot 33, the relationship between thedisplacements and forces working on the foot 33 and the knee joint 35can be defined and described by a matrix.

As shown in FIG. 1 and FIG. 2, assuming the foot 33 on the planarsurface 36 is subjected to a force representing as [F_(X), F_(Y)], suchforce [F_(X), F_(Y)] is detected by the sensing module 10 so as to befed into the parameter calculation module 21 of the force/trackdetection module 20. Moreover, the stiffness of the foot 33 is an 2×2endpoint stiffness matrix obtained by performing a partial differentialcalculation upon the known restoring forces and displacements in thex-axis and y-axis directions. It is noted that the aforesaid stiffnessmatrix can not be a diagonal matrix or a determinate matrix that is azero matrix. For keeping the entire lower limbs shown in FIG. 2 to movewithout tipping off and the whole chain of motions in the lower limbs ina stable balance state, the aforesaid stiffness matrix must be asymmetric matrix, i.e. S_(xy)=S_(yx), that is defined in the followingequations:

${F_{X}} = {{- ( {{\frac{\partial F_{X}}{\partial X}{X}} + {\frac{\partial F_{X}}{\partial Y}{Y}}} )} = {- ( {{S_{XX}{X}} + {S_{XY}{Y}}} )}}$${F_{Y}} = {{- ( {{\frac{\partial F_{Y}}{\partial X}{X}} + {\frac{\partial F_{Y}}{\partial Y}{Y}}} )} = {{- {( {{S_{XY}{X}} + {S_{YY}{Y}}} )\begin{bmatrix}{F_{X}} \\{F_{Y}}\end{bmatrix}}} = {{- {\begin{bmatrix}\frac{\partial F_{X}}{\partial X} & \frac{\partial F_{X}}{\partial Y} \\\frac{\partial F_{Y}}{\partial X} & \frac{\partial F_{Y}}{\partial Y}\end{bmatrix}\begin{bmatrix}{X} \\{Y}\end{bmatrix}}} = {{- {{\lbrack S\rbrack \begin{bmatrix}{X} \\{Y}\end{bmatrix}}\lbrack S\rbrack}} = \begin{bmatrix}\frac{\partial F_{X}}{\partial X} & \frac{\partial F_{X}}{\partial Y} \\\frac{\partial F_{Y}}{\partial X} & \frac{\partial F_{Y}}{\partial Y}\end{bmatrix}}}}}$

where,

-   -   dF_(X) is the differentiation of F_(X) with respect to        displacement;    -   dF_(Y) is the differentiation of F_(Y) with respect to        displacement;    -   S_(XY)∂F_(x)/∂Y, is the partial differentiation of F_(X) with        respect to displacement in Y-axis direction;    -   S_(YX)∂F_(Y)/∂X, is the partial differentiation of F_(Y) with        respect to displacement in X-axis direction;    -   S_(XX)=∂F_(X)/∂X, is the partial differentiation of F_(X) with        respect to displacement in X-axis direction;    -   S_(YY)=∂F_(Y)/∂Y F_(Y), is the partial differentiation of with        respect to displacement in Y-axis direction; and    -   [S] is a stiffness matrix representing the stiffness of the foot        33.

Assuming the biped system shown in FIG. 2 is under a certain balancecontrol (0<φ<2π) whereas the foot on the planar surface 36 is affectedto moved in multiple directions with a minimum displacement, thedisplacement of the foot 33 can be represented as [dX,dY]=[cos φ,sin φ],and the restoring force in x-axis direction and y-axis direction can bedefined as following:

F _(X)=−(S _(XX) cos φ+S _(XY) sin φ)

F _(Y)=−(S _(YX) cos φ+S _(YY) sin φ)

When the direction of the displacement is varied from 0 degree to 360degrees, a stiffness ellipse can be obtained, as the one shown in FIG.3. In FIG. 3, the line C11 connecting a random point on the stiffnessellipse C1 and the center thereof indicates the magnitude of therestoring force required when the selected random point is shifted fromits current stable balance state by a unit displacement C21. Thestiffness ellipse C1 is defined by the following equation:

${\frac{F_{X}^{2}}{( {\sqrt{2}( {{S_{XX}\cos \; \phi} + {S_{XY}\sin \; \phi}} )} )^{2}} + \frac{F_{Y}^{2}}{( {\sqrt{2}( {{S_{YX}\cos \; \phi} + {S_{XY}\sin \; \phi}} )} )^{2}}} = 1$

In the stiffness ellipse C1, the long axis indicates the direction ofmaximum restoring force, so that it is referred as the maximal stiffnessaxis; and on the other hand, the short axis thereof is referred as theminimal stiffness axis. Let the length of the long axis C12 to be 2a andthat of the short axis C13 to be 2b, the following equation can beobtained:

2a=2√{square root over (2)}(S _(XX) cos φ+S _(XY) sin φ)

2b=2√{square root over (2)}(S _(YX) cos φ+S _(YY) sin φ)

Accordingly, under balance control, the length of the long axis C12should equal to that of the short axis C13, so that the stiffnessellipse C1 is substantially conformed to the circle C2.

The operation principle of motion track identification performed by themotion identification module 22 is described in the following withreference to FIG. 1. In a motion identification operation, when anstretching exercise is performed and detected by the sensing module 10,the sensor values corresponding to the stretching exercise will beissued by the sensing module 10 that are received by the parametercalculation module 21 to be used in a calculation for obtaining featureparameters relating to muscle power and joint force of the limbs. Then,the obtained feature parameters are fed to the motion identificationmodule 22, in which the match unit 22 is enabled to perform a matchingcalculation upon the feature parameters and the motion track models soas to obtain an optimal motion track corresponding to the featureparameters according to the similarity between the vector of the featureparameters and the compared motion track model, which can be representedby a probability P(X|C_(k)) in the present embodiment, andsimultaneously, a GMM (Gaussian Mixture Model) is used as the motiontrack model, which are defined by the following equation:

$\begin{matrix}{{P( X \middle| C_{k} )} = {\prod\limits_{t = 1}^{T}{P( x_{t} \middle| \Lambda_{k} )}}} \\{= {\sum\limits_{t = 1}^{T}{\log \; {P( x_{t} \middle| \Lambda_{k} )}}}} \\{= {\sum\limits_{t = 1}^{T}{\log( {\sum\limits_{m = 1}^{M_{k}}{w_{k,m}{N( {{x_{t};\mu_{k,m}},\sum_{k,m}} )}}} )}}} \\{= {\sum\limits_{t = 1}^{T}{\log \begin{pmatrix}{\sum\limits_{m = 1}^{M_{k}}{w_{k,m}\frac{{\sum_{k,m}}^{1/2}}{( {2\pi} )^{d}}\exp}} \\( {{- \frac{1}{2}}( {x_{t} - \mu_{k,m}} )^{T}{\sum_{k,m}^{- 1}( {x_{t} - \mu_{k,m}} )}} )\end{pmatrix}}}}\end{matrix}$

wherein,

-   -   X={x₁, x₂, . . . , x_(t), . . . , x_(T)} represents the vector        of feature parameters obtained from the calculation of the        parameter calculation module that is fed into the motion        identification module, and x_(t)εR^(d);    -   C_(k) represents the k^(th) type movement in a motion track;    -   Λ_(k) represents the model parameters of the k^(th) type of        movement;    -   P(x_(t)|Λ_(k) is the probability model for each type of        movement, being a GMM        (Gausian Mixture model) that it is composed of: weights w_(k,m)        for each mixture, expected value vectors μ_(k,m) of each        Gaussian distribution, and covariance matrixes Σ_(k,m), whereas        those parameters and weights are obtained using an        Expectation-Maximaization (EM) algorithm.

For further modeling a motion for identification, a Latent SemanticAnalysis (LSA) is introduced for converting those feature parameters toan identification space where they can be trained to be used in a laterclassification process, as that shown in FIG. 4, by that the projectiondirection D1 of maximal identification effectiveness is found.

In the present embodiment, a method of singular value decomposition(SVD) is used for locating the optimal discriminative matrix T^(T) to beused for converting those feature parameters to the identificationspace, and then training the model parameters Λ_(k) of each C_(k) forfitting the result with a Gaussian Mixture Model so as to achieve theaforesaid modeling.

The aforesaid algorithm is defined by the following equation:

${{P( X \middle| C_{k} )} \approx {P( {T^{T}X} \middle| C_{k} )}} = {{\prod\limits_{t = 1}^{T}{P( {\hat{x}}_{t} \middle| {\hat{\Lambda}}_{k} )}} = {{\sum\limits_{t = 1}^{T}{\log \; {P( {\hat{x}}_{t} \middle| {\hat{\Lambda}}_{k} )}}} = {{\sum\limits_{t = 1}^{T}{\log( {\sum\limits_{m = 1}^{M_{k}}{{\hat{w}}_{k,m}{N( {{{\hat{x}}_{t};{\hat{\mu}}_{k,m}},{\hat{\sum}}_{k,m}} )}}} )}} = {\sum\limits_{t = 1}^{T}{\log {\quad( {\sum\limits_{m = 1}^{M_{k}}{{\hat{w}}_{k,m}\frac{{{\hat{\sum}}_{k,m}}^{1//2}}{( {2\pi} )^{d}}{\exp( {{- \frac{1}{2}}( {{\hat{x}}_{t} - {\hat{\mu}}_{k,m}} )^{T}{{\hat{\sum}}_{k,m}^{- 1}( {{\hat{x}}_{t} - {\hat{\mu}}_{k,m}} )}} )}}} )}}}}}}$

wherein {circumflex over (x)}_(t) and {circumflex over(Λ)}_(k)={ŵN_(k,m), {circumflex over (μ)}_(k,m), {circumflex over(Σ)}_(k,m); 1≦m≦M_(k)} represent the feature parameters and the modelparameters in the identification space, by that, during theclassification, the model similarity calculation is performed using theprobability model P(X|C_(k))≈P(T^(T) X|C_(k)) that is projected in theidentification space.

Please refer to FIG. 5 to FIG. 7, which show a motion of a lower limbwith respect to the variation of force direction during the motionaccording to the present disclosure. According to the monitoring systemshown in FIG. 1 when it is used for detecting the motion of lower limbs,it can use a pressure mat as its pressure detecting unit 11 whileattaching an accelerometer or gyroscope to be used as the positiondetecting unit 12.

In FIG. 5, the body mass center, i.e. the waist, is defined to be theend point of an lower-limb model, in which:

F_(X): basing on F=ma, it is the product of body mass with the secondorder time differentiation of body gravity center movement;

F_(Y): basing on the Newton's third laws of motion, it is the product ofthe sensing area with the total instant pressure detected by thepressure mat.

Please refer to FIG. 6, which is a schematic diagram showing thedetection of a lower limb motion according to an embodiment of thepresent disclosure. In FIG. 6, P1 is the mass center of the body; D isthe total displacement length of the body mass center P1 during thestanding-up movement in centimeter (cm); D_(x) is the length ofdisplacement in X-axis direction in centimeter (cm); D_(y) is the lengthof displacement in Y-axis direction in centimeter (cm); and θ_(d) is theincluded angle between D and D_(x).

Please refer to FIG. 7, which is a schematic diagram showing how thejoint angles in lower limb are related to each other. In FIG. 7, L_(f)is the length of the thigh; L_(t) is the length of the shank; θ_(H) isthe stretching angle of the hip joint; θ_(K) is the stretching angle ofthe knee joint; T_(Hip) is the torque exerting on the hip joint; andT_(knee) is the torque exerting on the knee joint. Thereby, therelationship of angle variation with respect to the forces and torquesexerting on the joints is defined as following:

$\begin{bmatrix}T_{Hip} \\T_{Knee}\end{bmatrix} = {{\begin{bmatrix}F_{X} \\F_{Y}\end{bmatrix} \cdot J^{T}} = {\lbrack S\rbrack \begin{bmatrix}{X} \\{Y}\end{bmatrix}}}$ $J = \begin{bmatrix}{{{- L_{f}}{\sin ( \theta_{H} )}} - {L_{t}{\sin ( {\theta_{H} + \theta_{K}} )}}} & {{- L_{t}}{\sin ( {\theta_{H} + \theta_{K}} )}} \\{{L_{f}{\cos ( \theta_{H} )}} + {L_{t}{\cos ( {\theta_{H} + \theta_{K}} )}}} & {L_{t}{\cos ( {\theta_{H} + \theta_{K}} )}}\end{bmatrix}$

Please refer to FIG. 8 and FIG. 9, which show a motion of an upper limbwith respect to the variation of force direction during the motionaccording to the present disclosure. In FIG. 8, the motion performed bythe upper limb is a racket swinging movement. According to themonitoring system shown in FIG. 1 when it is used for detecting themotion of upper limb, it can attach accelerometers at the user'sshoulder joint, elbow joint, and wrist joint to be used as the positiondetecting units 12, that are able to detect the smoothness of the racketswinging movement in a continuous manner.

In FIG. 8, D is the average wrist displacement length during the racketswinging movement in centimeter (cm); D_(x) is the length ofdisplacement in X-axis direction in centimeter (cm); D_(y) is the lengthof displacement in Y-axis direction in centimeter (cm); and θ_(d) is theincluded angle between D and D_(x).

In FIG. 9, Ls is the length of the upper arm; Lt is the length of thelower arm; θs is the included angle between the shoulder joint and theelbow joint; θ_(E) is the included angle between the upper arm and thelower arm; T_(shoulder) is the torque exerting on the shoulder joint;and T_(elbow) is the torque exerting on the elbow joint. Thereby, therelationship of angle variation with respect to the forces and torquesexerting on the joints is defined as following:

$\begin{bmatrix}T_{shoulder} \\T_{elbow}\end{bmatrix} = {{\begin{bmatrix}F_{X} \\F_{Y}\end{bmatrix} \cdot J^{T}} = {\lbrack S\rbrack \begin{bmatrix}{X} \\{Y}\end{bmatrix}}}$ $J = \begin{bmatrix}{{{- L_{S}}{\sin ( \theta_{S} )}} - {L_{E}{\sin ( {\theta_{S} + \theta_{E}} )}}} & {{- L_{E}}{\sin ( {\theta_{S} + \theta_{E}} )}} \\{{L_{S}{\cos ( \theta_{S} )}} + {L_{E}{\cos ( {\theta_{S} + \theta_{E}} )}}} & {L_{E}{\cos ( {\theta_{S} + \theta_{E}} )}}\end{bmatrix}$

The system and method disclosed in the present disclosure not only canbe applied in the field of exercise, but also can be applied in medicalapplications, such as the risk quantification and comparison for theelders or the mobility disabled.

By using the monitoring system shown in FIG. 1 in a manner that: thereare pressure mats being used as the pressure detecting unit 11 anddisposed at locations of highest risk of falling down, such as areasnear the gateway of a stair, bed sides, areas in front of a stool, andthere are accelerometers or gyroscopes being used as the positiondetecting units and attached to the belt of the user for detectinginformation relating to the user's movement. Thereby, sport-relatedfitness parameters can be estimated and calculated by the parametercalculation module 21 and the motion identification module 22 to be usedin a risk assessment for quantifying the risk of falling down in astepwise manner, i.e. high risk, middle risk and low risk, so that assoon as an assessment indicating high risk, the system will be enabledto issue a warning signal in a wired or wireless manner.

Regarding to the risk quantification and comparison and assuming thereare q risk assessment indexes of falling representing as y=└y₁, . . . ,y_(q)┘, and there are p parameters in the sport-related fitnessparameters representing as x=└x₁, . . . , x_(p)┘, the method for riskquantification and comparison is designed to find the parameters in thep sport-related fitness parameters that are most correlated with therisk assessment indexes. According to the monitoring system shown inFIG. 1, after receiving the fitness parameters estimated and obtainedfrom the match unit 222, the comparison unit 224 is enabled to comparethe estimated fitness parameters with the skill-related fitnessparameters stored in the fitness parameter database 223 so as to outputa comparison result accordingly; and if the comparison result is in theneighborhood of a high risk value of falling down, the notification unit225 is enabled to issue a warning signal.

The canonical correlation analysis is used for finding the fitnessparameters that are most correlated with the risk assessment indexes.That is, in statistics, canonical correlation analysis is a way ofmaking sense of cross-covariance matrices. If we have two sets ofvariables, {tilde over (x)}=a₁′x, a₁=└a_(1,1), . . . , a_(1,p)┘, {tildeover (y)}=b₁′y, b₁=└b_(1,1), . . . , b_(1,q)┘ and there are correlationsamong the variables, then canonical correlation analysis will enable usto find linear combinations of the {tilde over (x)} and the {tilde over(y)} which have maximum correlation with each other. That is, one seeksvectors maximizing the equation of a₁′Σ_(xy)b₁ subject to the constraintthat:

a ₁′Σ_(xx) a ₁=1b ₁′Σ_(yy) b ₁=1

and. Mathematically, the feature vector that is corresponded to thelargest eigenvalue λ₁ of Σ_(xx) ⁻¹Σ_(xy)Σ_(yy) ⁻¹Σ_(yx) is a₁ whereasthe eigenvalue λ₁ is able the largest eigenvalue Σ_(yy) ⁻¹Σ_(yx)Σ_(xx)⁻¹Σ_(xy) of whose corresponding feature vector will be 1.

To sum up, the system and method for monitoring sport related fitness byestimating muscle power and joint force of limbs has the followingadvantages:

(1) novelty:

A. it can use an information of two-dimensional (2D) pressuring sensingand displacement detection to estimate the movement of limbs and thechanging of body gravity center in three-dimensional space.

B. It can quantify the fitness parameter relating to muscle power andjoint force of limbs.

(2) Inventiveness:

A. It is an inventive software technique with fewer sensors required tobe worn on a user.

B. By the cooperation of the ergonomic model and the fitness parameterdatabase in the monitoring system of the present disclosure, the musclepower and joint force of limbs can be accurately estimated.

(3) Usability:

A. The monitoring system not only can be applied in the exerciseequipment industry for improving fitness or rehabilitation, but also canbe applied in medical applications, such as elder care for lower therisk of falling down.

With respect to the above description then, it is to be realized thatthe optimum dimensional relationships for the parts of the disclosure,to include variations in size, materials, shape, form, function andmanner of operation, assembly and use, are deemed readily apparent andobvious to one skilled in the art, and all equivalent relationships tothose illustrated in the drawings and described in the specification areintended to be encompassed by the present disclosure.

1. A system for monitoring sport related fitness by estimating musclepower and joint force of limbs, comprising: a sensing module, fordetecting sensor values; and a force/track detection module, forreceiving the sensor values from the sensing module; wherein, sensorvalues from the sensing module are fed to the force/track detectionmodule to be used as base for estimating feature parameters andclassifying a motion series relating to muscle power and joint force ofthe limbs so as to obtain fitness parameters corresponding to thesensing of the sensor module.
 2. The system of claim 1, wherein thelimbs include at least one type of limbs selected from the groupconsisting of: upper limbs and lower limbs.
 3. The system of claim 1,wherein the force/track detection module is enabled to tracking anexercise performed by the limbs, including upper limbs and lower limbs,while the detected track is resulting from at least one exerciseselecting from the group consisting of: the stretching exercise of theupper limbs and the stretching exercise of the lower limbs.
 4. Thesystem of claim 3, wherein the stretching exercise of the upper limbsincludes at least one movement selected from the group consisting of:lifting a heavy object, pushing/pulling, punching, racket swinging, andpitching.
 5. The system of claim 3, wherein the stretching exercise ofthe lower limbs includes at least one movement selected from the groupconsisting of: one foot lifting, one leg squatting, one leg standing,two legs standing.
 6. The system of claim 1, wherein the sensing moduleuses at least one device selected from the group consisting of: apressure detecting unit, and a position detecting unit, for detectingthe sensor values.
 7. The system of claim 6, wherein the pressuredetecting unit is a device selected from the group consisting of: apressure mat, a foot pad, and a force plate.
 8. The system of claim 6,wherein the position detecting unit includes at least one deviceselected from the group consisting of: accelerometers and gyroscopes. 9.The system of claim 8, wherein each device included in the positiondetecting unit is adapted to be arranged at a position selected from thegroup consisting of: an arm, an elbow, waist, and a knee of a user. 10.The system of claim 6, wherein the sensing module further comprises: aprocessing unit, for fetching and synchronizing the sensor values. 11.The system of claim 1, wherein the force/track detection module furthercomprises: a parameter calculation module, for fetching the sensorvalues from the sensing module to be used in a calculation for obtaininga signal capable of defining the tracking of the motion series in thespace, as well as in an estimation for obtaining feature parametersrelating to muscle power and joint force of the limbs; and a motionidentification module, for performing a matching calculation upon thefeature parameter resulting from the estimation of the parametercalculation module and at least one motion track model so as to performa classification basing on the comparison and thus obtain the motionseries to be used for estimating the fitness parameters.
 12. The systemof claim 11, wherein the parameter calculation module further comprises:a feature capturing unit, for fetching the sensor values from thesensing module to be used in an estimation for obtaining the featureparameters relating to muscle power and joint force of the limbs. 13.The system of claim 11, wherein the parameter calculation module furthercomprises: an ergonomic model database, for storing data relating toergonomic models to be used as basis for estimating the featureparameters.
 14. The system of claim 11, wherein the motionidentification module further comprises: a motion model database, havingthe at least one motion track model stored therein.
 15. The system ofclaim 14, wherein each motion track model is at least one model selectedfrom the group consisting of: a motion track template, a motion trackstatistic model, a motion track probabilistic model.
 16. The system ofclaim 11, wherein the motion identification module further comprises: amatch unit, for performing the matching calculation upon the featureparameters resulting from the estimation of the parameter calculationmodule and the at least one motion track model so as to obtain anoptimal motion track corresponding to the feature parameters to beclassified into the motion series that is used for estimating thefitness parameters.
 17. The system of claim 11, wherein the matchingcalculation performed by the motion identification module is at leastone calculation selected from the group consisting of: a distancecalculation and a similarity calculation.
 18. The system of claim 11,wherein the motion identification module further comprises: a fitnessparameter database, for storing skill-related fitness parameters whileproviding the stored skill-related fitness parameters to be comparedwith the fitness parameters obtained from the estimation of the motionidentification module.
 19. The system of claim 18, wherein the motionidentification module further comprises: a comparison unit, forcomparing the fitness parameters obtained from the estimation of themotion identification module with the skill-related fitness parametersstored in the fitness parameter database so as to output a comparisonresult accordingly.
 20. The system of claim 19, wherein the motionidentification module further comprises: a notification unit,electrically connected to the comparison unit to be used for displayingthe comparison result.
 21. The system of claim 20, wherein thenotification unit is a device selected from the group consisting of:cellular phones, personal digital assistants (PDAs), computers,speakers, alarms, indication lamps, and other audio/video devices. 22.The system of claim 20, wherein the notification unit is connected tothe comparison unit through a network system.
 23. The system of claim 1,wherein each feature parameter is related to at least one value selectedfrom the group consisting of: gravity center of human body, biped centerof mass, displacement of the center of gravity, direction, speed,distance traveled, relative position, coefficient of rigidity, jointangle, variation of joint angle, muscle power.
 24. The system of claim1, wherein the fitness parameter is related to at least one valueselected from the group consisting of: muscular strength, muscularendurance, agility, flexibility, muscle power, speed, and balance.25-48. (canceled)